Group Title: Working paper - International Agricultural Trade and Policy Center. University of Florida ; WPTC 06-05
Title: Decision support system for soybean rust (Phakopsora pachyrhizi) management using QnD
CITATION PDF VIEWER THUMBNAILS PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00089812/00001
 Material Information
Title: Decision support system for soybean rust (Phakopsora pachyrhizi) management using QnD
Series Title: Working paper - International Agricultural Trade and Policy Center. University of Florida ; WPTC 06-05
Physical Description: Book
Language: English
Creator: Kiker, Greg
Ranjan, Ram
Publisher: International Agricultural Trade and Policy Center. University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
 Record Information
Bibliographic ID: UF00089812
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.

Downloads

This item has the following downloads:

WPTC_06-05 ( PDF )


Full Text

WPTC 06-05


I ional Agricultural Trade and Policy Center




Decision Support System for Soybean Rust (Phakopsora
pachyrhizi) Management using QnD

By

Greg Kiker & Ram Ranjan

A7T"v- "'< "r>-


WORKING PAPER SERIES


} / y











UNIVERSITY OF
FLORIDA
Institute of Food and Agricultural Sciences
Institute of rood and Agricultural Sciences









INTERNATIONAL AGRICULTURAL TRADE AND POLICY CENTER

THE INTERNATIONAL AGRICULTURAL TRADE AND POLICY CENTER
(IATPC)


The International Agricultural Trade and Policy Center (IATPC) was established in 1990
in the Institute of Food and Agriculture Sciences (IFAS) at the University of Florida
(UF). The mission of the Center is to conduct a multi-disciplinary research, education and
outreach program with a major focus on issues that influence competitiveness of specialty
crop agriculture in support of consumers, industry, resource owners and policy makers.
The Center facilitates collaborative research, education and outreach programs across
colleges of the university, with other universities and with state, national and
international organizations. The Center's objectives are to:

* Serve as the University-wide focal point for research on international trade,
domestic and foreign legal and policy issues influencing specialty crop agriculture.
* Support initiatives that enable a better understanding of state, U.S. and international
policy issues impacting the competitiveness of specialty crops locally, nationally,
and internationally.
* Serve as a nation-wide resource for research on public policy issues concerning
specialty crops.
* Disseminate research results to, and interact with, policymakers; research, business,
industry, and resource groups; and state, federal, and international agencies to
facilitate the policy debate on specialty crop issues.











Decision Support System for Soybean Rust (Phakopsora
pachyrhizi) Management using QnD






Greg Kiker
Agricultural and Biological Engineering Department
P.O. Box 110570, Gainesville, Fl 32611-0570
Email:gkiker@ufl.edu, Phone: (352) 392-1864-291


&

Ram Ranjan
Postdoctoral Associate
International Agricultural Trade and Policy Center
Department of Food and Resource Economics, University of Florida
Email: rranian(@,ifas.ufl.edu, Ph: (352) 392 1881-326; Fax: (352) 392 9898


Selected paper prepared for presentation at the AAEA Annual Meeting in Long Beach
California 2006, July 23-26, 2006.






Copyright 2006 by Greg Kiker and Ram Ranjan. All Rights Reserved. Readers may
make verbatim copies of this paper for non-commercial purposes by any means, provided
that this copyright notice appears on all such copies.












Abstract


The objective of this project is to design a decision support system for soybean rust

management using gaming software that incorporates farmer's decision making in the

face of risks from soybean rust. Learning from past actions and neighbor's actions are

also incorporated. Farmers observe rust outbreak in the current and past periods and

decide over how much of land to allocate between soybean, corn and other crops. This

decision is influenced by maximization of expected profits criterion which entails crop

rotation choices that are based upon perceived risks, yield drags and input costs from

altering optimum rotation patterns. Adoption of new technology in terms of selecting

better rust management practices is also analyzed in an adaptive management framework.

The software meets the need of guiding policy formulation besides training stakeholders

in making economically sound choices in the absence of empirical data over pest

infestation.











Introduction

Soybean rust, a disease of the soybean and several other plant species, has been

threatening the US soybean crop since it arrived in 2004. Though the threat was reduced

in 2005 due to limited infestations during the crop season, potential for the pest becoming

endemic are serious and call for long term planning to manage this pest. Soybean rust is

chiefly windborne and is capable of trans-continental migrations helped by favorable

events such as hurricanes. In fact, hurricane Ivan of 2004 is suspected for bringing

soybean rust from South America. Soybean rust could cause significant damages to the

US soybean crops and available estimates in the literature project losses of up to US $7.2

billion/year from the disease (APHIS USDA 2004).

Management of soybean rust would require significant private participation

involving soybean growing farmers in the affected States and collaboration amongst

various States (and their respective area specialists) in order to monitor and control its

seasonal migration across regions. Due to its ability to survive in cool and wet climates,

it is possible for the rust to over-winter in the Southern Sates and infest soybean crops

during the growing season. Kudzu, a secondary host of the rust, is predominantly found

in the Southern States and could greatly assist in the long term establishment of this pest.

Management of soybean rust would require understanding the cropping decisions,

preventive and curative decisions and insurance options for the farmers and being able to

influence such choices through timely policy interventions. Crop rotations, such as

switching between soybean and corn (or other crops) and adequate precautionary steps

such as spraying of plants with fungicides could significantly reduce the damages from

soybean rust. Yet, crop rotations are a function of several economic criteria such as









differential economic yield between various crops per acre, crop prices, yield drags and

additional input costs involved in sub-optimum crop rotations and the risk perception of

the farmers. Similarly, decisions over how much or whether or not to spray are

influenced by risk perceptions and could vary from location to location based upon

farmer and regional heterogeneity. Preventive versus curative spraying is an additional

choice the farmers could exercise. Adaptive management of crops faced with the threat of

invasion can be expedited by public polices that reward socially optimum practices. For

this to be possible, an understanding of farmer's learning capabilities under various

infestation scenarios is crucial as it would help policy makers be a leg up in terms of

public inducement programs. One crucial learning process could be the decision over

preventive spraying based upon the latest spore finding at a location of x miles from the

farmer's plot. This distance is bound to stabilize over time through learning and

adaptation.

Soybean rust requires a paradigm shift in invasive species management. Invasive

species must be tackled at their source of introduction rather than waiting for them to

show up in regions where they could be potentially harmful to agricultural crops. Kudzu,

a secondary host of the sbr, is a key plant that could ensure its survival in the winter

season, especially in South Florida. Therefore, there is a need to understand the science

behind the chances of survival of sbr in the South and incorporate that into a decision

support tool.

While some work has already been done (Livingston et al. 2004, Roberts et al.

2006), that predicts the damages from sbr under various control scenarios, the literature is

still lacking in the knowledge over the capability of farmers to manage the risk of sbr and









being able to learn quickly to adapt to such risks through change in cropping pattern and

sbr control technology. Further, there is also a need to help the farmers get trained in rust

management by getting sound scientific advice over the sbr spread probabilities and the

choice of management tools that optimize economic returns. Due to lack of empirical

evidence of rust impact within United States, real time tracking and guidance is the key to

managing sbr. Consequently, there is a need for software that could keep abreast of year

to year seasonal spread of sbr and provide guidance to stakeholders over the choice and

timing of management tools. This paper presents the details of a software being

developed to meet the above mentioned needs. The methodology involves relating

farmer's actions in terms of agricultural and invasive species management choices to

consequences over profitability and pest spread outcomes. The biology of sbr spread and

its impact over crops provides the crucial linkage between management choices and

outcomes. By repeating the management outcomes over hypothetical scenarios that are

grounded in real time observations, the tool offers a trial and error type learning support

for the stakeholders. Simulating decisions based upon spatial and temporal spread of the

pest and also upon the actions of neighboring farmers, the behavioral responses of the

farmer could be brought to light. Knowledge of learning behavior could provide crucial

feedbacks to policy makers and guide the choice of policies that further enforce such

behavior.

Before proceeding to the model, a brief review of the biological and economic

issues related to soybean rust is in order. After which, we present a detailed explanation

of the QnD software, the model, the assumptions involved and some preliminary results.



Biological Background









Soybean rust is a fungal species. There are two types of this species; Phakopsora

pachyrhizi (Asian rust) and the Phakopsora meibomiae (The new world type). It is,

however, not easy to distinguish between the two species without the use of molecular

techniques (Sweets 2002). Soybean rust mostly affects the leaves of the host plant,

producing powdery pores that reduce the photosynthetic capability of the plant thus

causing reduction in seed numbers and weight (Sweets 2002). Of the two, the Asian rust

has been found to be more damaging. This is found in Japan, Australia, central and

southern Africa, etc. The new world type is found in the Caribbean and Central and

South America (Sweets, 2002). Soybean rust was first detected in Japan in 1902, from

where it spread to other parts of Asia (India 1951), Australia, Africa (Kenya, Rwanda,

and Uganda, 1996) and South America. It was detected in Hawaii in 1994.

Soybean rust has already arrived in the US, detected first in Southern US in

20041. Its current day to day status is being monitored by APHIS and can be found at

USDA's soybean rust website (at www.sbrusa.net). As of November 2005, North

Carolina had 14 infected counties, Alabama 29, Florida 23, Georgia 34, Mississippi 2,

South Carolina 18, North Carolina 14, and Louisiana had 1 infected county.

Soybean rust is chiefly windbome and the pores produced by the fungus can be

readily carried through wind and deposited in locations very far way. The Asian soybean

rust which affects 95 species of plants including Soybean, has drastically affected crop

yields in Asia. These spores quickly establish themselves in new environments under

favorable conditions (Nagarajan and Singh 1990). The ideal conditions of the spread of

soybean rust are cool (below 82 F) and wet weather. There are 30 species in 17 genera of

1 "The most likely scenario as to how soybean rust arrived in the continental United States is via Hurricane
Ivan. Ivan formed in the Atlantic in early September, brushed the South American coast, and proceeded to
strike the southeastern United States, carrying rust spores from Colombia and Venezuela" (Hart 2005).









legumes that are hosts to soybean rust. In the US, Kudzu is considered to be a potential

host to the rust2. The establishment of soybean rust could have serious implications for

the US agriculture. Consequently, economic factors linked to its spread and damages are

of key importance.



Economic Issues Related to Rust Management

Cropping patterns can severely influence damages from soybean rust. In South Africa in

2001, the loss in yield from soybean rust was up to a hundred percent in regions where

farmers did not rotate their crops and practiced mono-cropping (APHIS USDA 2004). In

the USA, total losses to crop yield may reach up to 50% in regions where climate is

conducive to their growth. Projections of economic damages reveal a loss of US $7.2

billion/year from the disease (APHIS USDA 2004). Computer simulations have predicted

yield losses up to fifty percent in Southern Florida due to its warm climate (Corn and

Soybean Digest 2003). The South American Countries lost $1 billion in 2002 to Soybean

Rust (Lamp, 2003). Brazil, which harvested $11.5 billion worth of soybean crops in

2002-2003, had nearly 80% percent of its soybean crops treated with fungicides. This led

to an added cost of $40-50 per hectare to its production costs (Reuters, November 14

2003).

Fungicides have been found to be effective but may prove costly to small farmers.

Other methods of its control include host eradication (weeds, etc.), biological control and

development of more resistant soybean varieties. The cost of fungicide application may


2 More information related to Kudzu population in the US and rust findings in those areas can be found at
the University of Illinois at Urbana Champaign's website: (http://sovrust.cropsci.uiuc.edu/ed mat/rust-
confirmations.pdf).









be enormous for marginal farmers as it might take up to three applications at a cost of

$15 per acre to control the pest (Corn and Soybean Digest 2003)3. There is very limited

scope for preventive efforts as the pores have the ability to transport themselves through

wind over vast measures of space. Preventive measures may be ineffective for two

reasons. First, there are no known soybean varieties that have genetic resistance to the

pest. Further, the conditions suitable to soybean have also been found to be suitable to

the rust (Corn and Soybean Digest, 2003).



Soybean and Corn Yield Functions

In our model, the State-specific yield functions for soybean and corn are based upon an

ERS report by Teigen and Thomas (Teigen and Thomas 1995). The non-linear

relationships between temperature and precipitation explain most of variations in the

yields in corn and Soybean (Teigen and Thomas 1995). Using this approach, Teigen and

Thomas estimate the yield functions for soybean and corn for the primary production

states within the US. The data set consists of an aggregated monthly temperature and

precipitation value at the State and regional levels. Other key factors included in the

model are time trend and acreage. They find that the corn yields have increased at an

average of about 1.8 bushels per year for the period of 1950-1993. The impact of rainfall

is significant for the months of July through August on crop yields, whereas the months

of Jan through May have insignificant impacts. Both the temperature and precipitation


"The Environmental Protection Agency (EPA) has registered three chemicals--azoxystrobin,
chlorothalonil, and pyraclostrobin--for the treatment of soybean rust. These chemicals are preventative
treatments in that they protect soybean plants from infestation and limit subsequent rust development.
Soybean rust spreads by spores. There are, however, restrictions on the extent and number of applications
of these chemicals and not all States approve these chemicals." (Hart 2005)









impacts on yields are quadratic in nature. The rainfall and temperature data is converted

into Z-scores which represent variation from the long term mean divided by the standard

deviation.



Soybean and Corn Prices

Historical prices for Soybean and Corn are available at the NASS. The US is no longer

the dominant producer of soybean and its prices are now jointly determined by the South

American production and the US stock to use ratio. An ERS study finds that an increase

in one percent of South American soybean production depresses US prices by .25

percent. This effect includes the negative impact of South American production of

soybean on US stock to use ratio (.4 percent to every 1 percent change in South American

production) and the subsequent impact of reduction in stock to use ratio on US prices (.5

percent to every one percent change in stock to use ratio).



Options for sbr management

Acreage Allocations between Soybean and Corn

The risk of economic loss from soybean rust can be mitigated by planting corn (and other

crops) in place of soybean. The current practice of a 50:50 corn: soybean crop rotation

reduces the need for soil amendments and helps maintain soil fertility and yield. The

decision to increase the corn rotation will also depend on relative crop prices and the

effect of the rotation on input costs and yields.

Treatment Decisionsfor Soybean Rust









Both preventative (pre-infection) and curative (post-infection) fungicide spray options are

available for treating soybean rust. Application timing is critical in the effectiveness of

these options. Daily spore monitoring reports can aid farmer preparedness. Farmers can

purchase insurance against sbr, however reimbursement for damages may dictate that the

farmer follow 'good management practices,' i.e. timely fungicide application. The

various stages of soybean plant growth are classified as VI through Vn and R1 through

R8, where V stands for the vegetative stage and R for the reproductive stage. It is the

reproductive stage of the plant growth when it is most vulnerable to infestation from the

rust. R1-R2 are the flowering stages, R3-R4 for pod development, R5-R6 seed

development and R7-R8 are the maturity stages. Late R4 through the early R6 stages are

the most vulnerable periods for rust infestation and fungicide application is most

recommended within this time period.

Insurance

There are two types of insurance available to farmers: group insurance and individual

insurance. Individual insurance reimburses the farmer for losses exceeding the

deductible. Estimation of losses is based on yield and revenue history. Group insurance

ties reimbursement payments to historic county yields and a minimum yield cutoff. Both

options may require farmers to follow disease prevention and protection protocol in the

event of a spore infestation. The insurance protection may erode over time if rust

becomes endemic and insurance premiums and deductibles rise.



QnD Software

Questions and Decisions Modeling System









The Questions and Decisions TM (QnDTM) model system (Kiker et al., 2006) was created

to provide an effective and efficient tool to integrate ecosystem, management, economic

and socio-political factors into a user-friendly model framework. The model is written in

object-oriented Java and can be deployed as a stand-alone program or as a web-based

(browser-accessed) applet. The QnD model links spatial components within geographic

information system (GIS) files to the abiotic (climatic) and biotic interactions that exist in

an environmental system.

The model can be constructed using any combination of detailed technical data or

estimated interactions of the ecological/management/social/economic forces influencing

an ecosystem. The model development is iterative and can be initiated quickly through

conversations with users or stakeholders. Model alterations and/or more detailed

processes can be added throughout the model development process. QnD can be used in

a rigorous modeling role to mimic system elements obtained from scientific data or it can

be used to create a "cartoon" style depiction of the system to promote greater learning

and discussion from decision participants.


































Figure 1: Screen capture from the QnD: SBR demonstration version (Kiker and Ranjan,

2006).

The QnD system has two parts: the game view and the simulation engine as shown in

Figure 2. The game view has several types of outputs that can be configured by the user

via XML (eXtensible Markup Language) file inputs. By presenting the outputs as

selectable, QnD allows users to choose how they want to see their output, including the

following output options:



GIS Maps that are updated on each time step

Warning lights that change at user-selected critical levels

Mouse-activated charts and text for individual spatial areas (pie charts and text

line descriptions)


I QDSBI jprk









Time-series charts (listed on several tabbed pages)

Text output files (in comma separated format)



The simulation engine of QnD is made of objects linked together into simple or complex

designs, determined by the needs of decision participants. The most elemental objects of

QnD are Components, Processes and Data. A Component is an object that is of interest

to the user. Processes are the actions that involve Components. Data are the descriptive

objects assigned to Components. If one uses parts of grammar as an analogy,

Components are the nouns. Processes are the verbs. Data objects are the adjectives or

adverbs. Components objects are spatially situated into the virtual QnD landscape and

can interact with each other over space and time. With the QnD object framework, both

simple and complex designs are possible. In more complex designs, building block

components and processes designed as clusters of subcomponents or sub-processes.

Upon startup, specialized internal QnD objects read the relevant XML input files and

create all the engine parts (Components, Processes and Data) as well as the game view

(maps, charts and management options) required for the simulation. Once all the

necessary parts are created, QnD is "played" much like any other computer games. Users

can manipulate the game view in the following ways:



Set some management options (using the slider bars)

View the map page and switch between maps (with radio buttons)

View the various Chart pages (with the chart tabs)

Simulate a time steps at user-defined levels










* Reset the game to the startup


"Simulation Engine"
* Developer's point of contact
* Creates information
* Objects: Components, Processes and Data
* Calculation for selected time step


"Game View"
* User/Player's point of contact
* Communicates information
*"Widgets": Maps, Charts, Warning Lights,
Text, Sliders, Icons, Buttons
* User choices management settings,
simulate fast or slow time step, reset




iA



--I ... +-
^-'*^^-^ .+.'2 .....'
^m^":


Figure 2. QnD model structure (from Kiker et al., 2006).



Management settings are applied to the current time step that is activated by mouse-

clicking on either of the two time step buttons. After clicking on the time-step button,

results of the simulation are applied to the various output devices (maps, charts, warning

lights, text files etc...). The user may explore the system outputs, choose new

management options and continue with the simulation. Certain end points can be created

to show various ramifications of management actions. In Kiker et al. (2006), QnD end

points showing ecosystem destruction, bankrupt financial status or employment









termination were used to show the various end points of ecosystem management in

African savanna ecosystems.



SCENARIO AND MODEL DEVELOPMENT



The QnD model has been developed as a useful tool embedded in a larger process of

stakeholder and public participation when utilized to generate questions and decisions for

complex environmental management (Kiker et al, 2006; Kiker and Linkov, 2006).

Development of a QnD game and its application is one potential way to view a complex

environmental problem situation from a variety of technical, social and cultural

perspectives.



QnD and Scenario Planning

A QnD scenario model can be used to facilitate dialogue and learning at several stages

through the scenario planning process (see Figure 3). The QnD development

methodology is flexible and responsive enough that it can be used iteratively throughout

the entire scenario development process, or as a quick snapshot at any one stage. The

extent of application is at the discretion of the scenario development team; the model

does not need to be included from the beginning, nor does it need to be used through the

entire process to the end. QnD provides unique benefits when used at various points of

scenario development, as discussed below.

Initially QnD can be used to assist with setting the agenda, at the same time that

individual interviews and group brainstorming is taking place. The model development









process becomes a form of analyzing the current situation by finding critical driving

forces and main concerns. Through the participative process used to create the model,

stakeholders discuss and debate the current situation. The result is a working model

which reveals the implications of qualitative and quantitative information, including

participants' assumptions and worldviews. The QnD model that is built during this initial

phase is called Version Zero.

As scenarios are being structured and story lines developed, the QnD Version

Zero can be adjusted to reflect the different worlds that are being created. The model is

useful at this stage of development to test the first generation of scenarios for internal

consistency and plausibility. The questions that need to be answered in order to build the

model and work with the game interface reveal any inconsistencies that exist in the story

lines. The model that is developed at this phase can be called Version One. While the

QnD model can be used as scenarios are being developed, the model can also be

developed when scenarios are already in place. Once scenario story lines are finalized,

QnD is used to create an interactive scenario environment. If Version Zero and Version

One were developed earlier in the process, then these versions are adjusted to reflect the

key drivers and story lines that have been chosen, resulting in Version Windtunnel. If

QnD is used for the first time at this stage, a new model is quickly developed around the

key drivers and story lines. QnD Version Windtunnel creates an interactive scenario

environment which is used to windtunnel or trial strategic options in order to determine

the implications of various potential decisions in the different scenario story lines. The

effects of various strategic options are reported as model results used to evaluate each

option against the conditions in each scenario story line. By interacting with the QnD










game interface, stakeholders are able to windtunnel potential management decisions,

searching for actions that are more robust when played out within the conditions of

different future worlds described in the scenarios.




The QnD Version Windtunnel continues to be used once implementation begins. As

action plans are implemented, the model is updated with monitoring data and the game

interface is used to trial changes to action plans. By using a QnD Scenario Model, the

future worlds created in the scenario story lines are maintained in a working game which

makes it possible to continually interact with the lessons learned during the scenario

development process. The lessons are not lost as key drivers and variables are available

in a useable format for stakeholders at all levels of decision making.



Setting the
Agenda
~-..~. ... Monitor apiiht
Sc---i-os -Implementation
External Internal
Agenda Agenda
(scenario) (current activities) Pr
Project
Z J Planning
Scenario
Development
Direction
Scenario
Structuring
HWindtumnel
Develop Intcrnal Agenda
Story Lines In Seanarios
Test First Revise
Generation Scenarios


Figure 3. Overview of the scenario planning process integrated with the QnD model.

































Local Components


Figure 4: Spatially explicit objects at the state scale using Florida as an example.


a *
*
**
*
U *
,
a * *
S .' *
S **
.:* .*~
.

a. *



External SB Rust Spores
eleoomeoeeeoleleeeoeeoieeee


SB Rust Host Area

+

Weather Conditions

(Warm + Wet)


Figure 5: Soybean Rust sources and spread diagram.


f
-






































Figure 6: Local Component Interactions within a state



































Figure 7. State and region -specific crop models are adapted from Teigen and Thomas

(1993). The weather effects and yield functions are listed for Iowa.









Results

The simulation analysis involves the farmer simulating his soybean yield and revenue

outcomes through the selection of a set of options which are both spatially and temporally

defined. Figure 8 shows an example set of QnD:SBR yield outputs for soybean for Iowa

and Florida spatial areas (upper graph) for one season. In addition, the lower graph

displays the various precipitation inputs in terms of the Z-Score (+3 for extreme wet

conditions, 0 for median conditions and -3 for extreme drought).


Soybean Yield
(bu/ac)


Precipitation Z-Score
+3 extreme wet
-3 extreme dry


Iowa



"A


Florida -



Time (months)


Figure 8. Example QnD:SBR results showing simulated soybean yields for Iowa and
Florida. In addition, the monthly precipitation Z-Score is provided to show the influence
of rainfall over the season.









Within the QnD:SBR model, the graphs in Figure 8 scroll along, showing the newest

month's simulation while displaying the last twelve months of climate and yield data.

Additional charts can show total production costs, corn yields, total profitability, soybean

rust sites and other data of interest to the player. QnD's modular structure allows any

data object to be displayed within a map, time series, warning light, text or output file

value. With this modularity, specific interface options can be created quickly to suit

various player preferences.

Using the integrated weather, yield and SBR information over each month, a

farmer in the Heartland region would have the option of observing the pest spread

through the southern regions over each month. His early decisions would involve crop

choices between soybean, corn and others. Once crops have been planted, his next set of

choices would be to scout the fields for SBR frequently and apply preventative spraying

promptly. He would also of the choice of purchasing insurance at appropriate times.

Finally, curative spraying would be applied. This process is simulated for a distribution

of pest spread which is randomly generated but adheres to the accepted limits within the

region. Other uncertain parameters over which the farmer may have no control are prices,

weather parameters and neighboring farmers' actions. Simulating over this entire range

of uncertain parameters trains the farmer in generating a range of outcomes and makes

him conversant with the consequences of his actions fairly quickly.

Upon full development, the model would be capable of performing the above

functions for all relevant regions in the US. Further, possible extensions to incorporate

use of advanced markets and application to other related invasive pests would be

explored. Some of these extensions are detailed below.









Future Extensions

Use of Futures and Options to Mitigate Risk

Increase in global supply combined with the threat of soybean rust for soybean has lead

to increased price fluctuations for soybean. After the first discovery of soybean rust,

there was a gain in futures price of soybean to as much as 40 cents in a few days time.

The eventual decline in futures price was brought about by the lack of any damages to

soybean that year. The impact on prices is basically determined by two forces. The

bullish trend from speculative forces and the bearish trend from increased production and

high stock to use ratio would eventually determine the level and volatility of soybean

prices. In order to minimize the risk from these fluctuations, the farmer would need to

combine good management practices with available market instruments. These include

insurance and advanced financial markets such as futures hedging, forward contracts, call

and put options etc. Historically, soybean futures have been traded on Chicago Board of

Trade. Buying futures in soybean takes place when prices are expected to rise and selling

takes place when they are expected to fall (See Schnepf et al. (1999) for a discussion of

all available insurance and non-insurance options for corn and soybean growers).



Links to other Invasive Pests

The benefits of a real time tool for aiding farmers in decision making under threat from

sbr cannot be overemphasized. The benefits may be even higher in the event of multiple

pest infestation that have consequences for the same group of crops or farmers. For

instance, the threat of avian flu influenza is a real one for the United States. The main

carrier of this virus, chickens, is also the largest consumer amongst livestock of soybean









products in the US. In 2000, soybean meal consumption by poultry amounted to about

44 percent of the total demand amongst the livestock. Arrival of avian flu would

definitely impact demand for soybean, thereby having an impact on soybean prices.

Having online software that reflects such impacts through hypothetical scenario analysis

could greatly enhance farmers' preparedness against invasive pests.









References

1. APHIS USDA (2004):

http://www.aphis.usda.gov/ppq/ep/soybean_rust/UreMelPp502.pdf

2. Corn and Soybean Digest (Dec 1, 2003): "Soybean Rust on the Move":

http://soybeandigest.com/ar/soybean_soybean rust move/

3. Hart, C. Agricultural Situation Spotlight: Preparing for Soybean Rust

http://soybeandigest.com/ar/soybean_soybeanrustmove/http://www.card.iastate.

edu/iowa ag review/spring_05/article3.aspx

4. Kiker, G.A., Rivers-Moore, N.A., M.K. Kiker and I. Linkov (2006): QnD: A

modeling game system for integrating environmental processes and practical

management decisions. (in Press) (Chapter in Morel, B. Linkov, I., (Eds) "The

Role of Risk Assessment in Environmental Security and Emergency Preparedness

in the Mediterranean". Kluewer, Amsterdam 2005.

5. Kiker, G.A. and I. Linkov (2006): The QnD Model/Game System: Integrating

Questions and Decisions for Multiple Stressors. (in Press) (Chapter in

Goncharova, N., Arapis, G., (Eds) "Ecotoxicology, Ecological Risk Assessment

and Multiple Stressors" Kluewer, Amsterdam 2005.

6. Kim, C.S., G. Schaible, L. Garrett, R. Lubowski, and D. Lee (2006 a): The U.S.

Soybean Industry: The Case of Soybean Aphid Infestation," Canadian J

Agricultural Economics, in review.

7. Kim, C. S., G. Schaible, L. Garrett, R. Lubowski, and D. Lee (2006 b): Biological

Invasions: The Case of Soybean Aphid Infestation, selected paper, American

Agric. Econ. Assoc, Long Beach, CA.









8. Lee, Donna J., C. S. Kim, G. Schaible (2006): Estimating the Cost of Invasive

Species on U.S. Agriculture: The U.S. Soybean Market, selected paper, American

Agric. Econ. Assoc, Long Beach, CA.

9. Livingston, M, R. Johanssson, S. Daberkow, M. Roberts, M. Ash, V. Breneman

(2004): Economic and policy implications of wind-borne entry of Asian soybean

rust into the United States, Electronic Outlook Report from the Economic

Research Service, OCS-04D-02.

10. Nagarajan, S., and D.V. Singh (1990): "Long-Distance Dispersion of Rust

Pathogens." Annual Review ofPhytopathology 28, 139-153.

11. Plato, G., and W. Chambers (2004): How Does structural Change in Global

Soybean Market Affect the US Price? OCS-04-D01, USDA ERS

12. Roberts, M.J., D. Schimmelpfennig, E. Ashley, M. Livingston, M. Ash, and U.

Vasavada (2006): "The Value of Plant Disease Early-Warning Systems: A Case

Study of USDA's Soybean Rust Coordinated Framework. Economic Research

Report No. (ERR-18).

13. Sweets, L. (2002): "Soybean Rust: What's the Concern?", Integrated Pest & Crop

Management Newsletter, University of Missouri-Columbia, Vol. 12, No. 26 ,

Article 1 of 3: http://ipm.missouri.edu/ipcm/archives/v12n26/ipmltrl.htm

14. Teigen, L.D., and M. Thomas Jr. (1995): "Weather and Yield, 1950-94,

Relationships, Distributions and Data", ERS Staff Paper.




University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs